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The Award database is continually updated throughout the year. As a result, data for FY20 is not expected to be complete until September, 2021.

  1. Bounding generalization risk for Deep Neural Networks

    SBC: Euler Scientific            Topic: NGA20A001

    Deep Neural Networks have become ubiquitous in the modern analysis of voluminous datasets with geometric symmetries. In the field of Particle Physics, experiments such as DUNE require the detection of particle signatures interacting within the detector, with analyses of over a billion 3D event images per channel each year; with typical setups containing over 150,000 different channels.  In an ...

    STTR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  2. IA 2: Intent-Capturing Annotations for Isolation and Assurance

    SBC: Immunant, Inc.            Topic: HR001120S0019001

    Software and hardware flaws can be exploited to make programs perform unintended computations or leak sensitive data. We propose to counter these threats by isolating libraries and other program units inside a single process. The developer will insert source-level annotations that i) map code and data units to compartments and ii) capture how each compartment is intended to interact with others, i ...

    STTR Phase I 2020 Department of DefenseDefense Advanced Research Projects Agency
  3. Algorithms for Look-down Infrared Target Exploitation

    SBC: SIGNATURE RESEARCH, INC.            Topic: NGA18A001

    The multidisciplinary area of GEOINT is changing and becoming more complex. A major driver of innovation in GEOINT collection and processing is artificial intelligence (AI). AI is being leveraged to help accomplish spatial analysis, change detection, and image or video triage tasks where filtering objects of interest from large volumes of data is critical. NGA is confronting the changing GEOINT l ...

    STTR Phase II 2020 Department of DefenseNational Geospatial-Intelligence Agency
  4. Visual Relative Navigation

    SBC: TOYON RESEARCH CORPORATION            Topic: ST18C006

    As unmanned aircraft systems (UAS) become more prevalent there is an increasing desire to automate UAS navigation and control. To enable future UASs to perform a wider variety of missions, they must be able to complete autonomous relative navigation to accomplish missions. Current technologies rely heavily on GPS measurements, which are undesirable since GPS signals may be unavailable in many DoD ...

    STTR Phase II 2020 Department of DefenseDefense Advanced Research Projects Agency
  5. Pathogen Classification Tool (PACT)

    SBC: Stottler Henke Associates, Inc.            Topic: ST18C002

    Stottler Henke proposes PACT to address the threat posed by unknown/novel bacteria. Stottler Henke’s solution leverages AI/ML technologies to assess the pathogenic potential of unknown/novel bacteria for DARPA’s Biological Technologies Office. Threat assessment is inferred from phenotype as characterized by a series of assays developed by Harvard University as part of DARPA’s Friend or Foe p ...

    STTR Phase II 2020 Department of DefenseDefense Advanced Research Projects Agency
  6. Visual Relative Navigation via Intelligent Ephemeral Relationships (VRNIER)

    SBC: TOYON RESEARCH CORPORATION            Topic: ST18C006

    As unmanned aircraft systems (UAS) become more prevalent there is an increasing desire to automate UAS navigation and control. To enable future UASs to perform a wider variety of missions, the they must be able to complete autonomous relative navigation to accomplish missions. Current technologies rely heavily on GPS measurements, which are undesirable since GPS signals may be unavailable in many ...

    STTR Phase I 2019 Department of DefenseDefense Advanced Research Projects Agency
  7. Pathogen Classification Tool (PaCT)

    SBC: Stottler Henke Associates, Inc.            Topic: ST18C002

    Stottler Henke proposes PaCT, leveraging our related past work in computer vision and machine learning. Drawing from techniques used in ExPATSS, a Phase II SBIR effort slated for transition to the Naval fleet, PaCT will perform bacterial characterization using features derived from the phenotype of the bacteria. PaCT will predict bacterial characteristics such as pathogenicity, antibiotic resistan ...

    STTR Phase I 2019 Department of DefenseDefense Advanced Research Projects Agency
  8. Development of Autonomous Glycemic Control Mechanism for Patients Suffering Glycemic Abnormalities as a Result of Critical Illnesses

    SBC: PROFUSA, INC.            Topic: ST18C004

    The use of continuous glucose monitors can be an invaluable management tool for patients afflicted by glycemic variability due to critical illness or trauma. Maintaining stable glucose levels enhances health and lowers care costs, and individuals equipped with continuous glucose data have significantly improved outcomes. Profusa has developed highly miniaturized, injectable, tissue-like, glucose s ...

    STTR Phase I 2019 Department of DefenseDefense Advanced Research Projects Agency
  9. Computational Biology Platform Technology for Cell Conversion and Differentiation

    SBC: IREPROGRAM, LLC            Topic: ST17C001

    Methods for interconversion between cell types (cellular reprogramming) are currently discovered through resource intensive trial and error. Experiments may test a multitude of transcription factors to identify correct combinations that influence cell fate. In addition, reprogramming approaches commonly use stem cell intermediates such as induced pluripotent stem cells (iPSCs), which are generated ...

    STTR Phase II 2019 Department of DefenseDefense Advanced Research Projects Agency
  10. Hybrid DNN-based Transfer Learning and CNN-based Supervised Learning for Object Recognition in Multi-modal Infrared Imagery

    SBC: TOYON RESEARCH CORPORATION            Topic: 1

    On this effort Toyon Research Corp. and The Pennsylvania State University are developing deep learning-based algorithms for object recognition and new class discovery in look-down infrared (IR) imagery. Our approach involves the development of a hybrid classifier that exploits both transfer learning and semi-supervised paradigms in order to maintain good generalization accuracy, especially when li ...

    STTR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
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